






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE							=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							=    {
    'BAAI/AltCLIP': 'https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json',
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}
class 				A_     (    __lowercase      ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE				:      Optional[Any]			=       "altclip_text_model"


 def __init__(						self				,	_A=250002				,	_A=1024				,	_A=24				,	_A=16				,	_A=4096				,	_A="gelu"				,	_A=0.1				,	_A=0.1				,	_A=514				,	_A=1				,	_A=0.02				,	_A=0.02				,	_A=1e-05				,	_A=1				,	_A=0				,	_A=2				,	_A="absolute"				,	_A=True				,	_A=768				,	**_A				,	)			->		Union[str, Any]:

     """simple docstring"""



     super().__init__(pad_token_id=_A				,	bos_token_id=_A				,	eos_token_id=_A				,	**_A)

     _UpperCAmelCase					:   Optional[Any]      	=					vocab_size
     _UpperCAmelCase					:   int      	=					hidden_size
     _UpperCAmelCase					:   Tuple      	=					num_hidden_layers
     _UpperCAmelCase					:   Optional[int]      	=					num_attention_heads
     _UpperCAmelCase					:   str      	=					hidden_act
     _UpperCAmelCase					:   Dict      	=					intermediate_size
     _UpperCAmelCase					:   int      	=					hidden_dropout_prob
     _UpperCAmelCase					:   Optional[Any]      	=					attention_probs_dropout_prob
     _UpperCAmelCase					:   Tuple      	=					max_position_embeddings
     _UpperCAmelCase					:   Tuple      	=					type_vocab_size
     _UpperCAmelCase					:   List[str]      	=					initializer_range
     _UpperCAmelCase					:   int      	=					initializer_factor
     _UpperCAmelCase					:   List[Any]      	=					layer_norm_eps
     _UpperCAmelCase					:   List[str]      	=					position_embedding_type
     _UpperCAmelCase					:   Union[str, Any]      	=					use_cache
     _UpperCAmelCase					:   int      	=					project_dim



class 				A_     (    __lowercase      ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE				:      Dict			=       "altclip_vision_model"


 def __init__(						self				,	_A=768				,	_A=3072				,	_A=512				,	_A=12				,	_A=12				,	_A=3				,	_A=224				,	_A=32				,	_A="quick_gelu"				,	_A=1e-5				,	_A=0.0				,	_A=0.02				,	_A=1.0				,	**_A				,	)			->		Optional[int]:

     """simple docstring"""



     super().__init__(**_A)

     _UpperCAmelCase					:   Union[str, Any]      	=					hidden_size
     _UpperCAmelCase					:   Any      	=					intermediate_size
     _UpperCAmelCase					:   int      	=					projection_dim
     _UpperCAmelCase					:   Union[str, Any]      	=					num_hidden_layers
     _UpperCAmelCase					:   List[Any]      	=					num_attention_heads
     _UpperCAmelCase					:   int      	=					num_channels
     _UpperCAmelCase					:   int      	=					patch_size
     _UpperCAmelCase					:   Dict      	=					image_size
     _UpperCAmelCase					:   str      	=					initializer_range
     _UpperCAmelCase					:   Any      	=					initializer_factor
     _UpperCAmelCase					:   Optional[Any]      	=					attention_dropout
     _UpperCAmelCase					:   Optional[Any]      	=					layer_norm_eps
     _UpperCAmelCase					:   Union[str, Any]      	=					hidden_act


 @classmethod
 def 			snake_case__					(						cls				,	_A				,	**_A)			->		"PretrainedConfig":

     """simple docstring"""



     cls._set_token_in_kwargs(_A)

     _UpperCAmelCase					:   Union[str, Any]      	=					cls.get_config_dict(_A				,	**_A)

     # get the vision config dict if we are loading from AltCLIPConfig
     if config_dict.get('''model_type''') == "altclip":
         _UpperCAmelCase					:   Optional[Any]      	=					config_dict['''vision_config''']

     if "model_type" in config_dict and hasattr(cls				,	'''model_type''') and config_dict["model_type"] != cls.model_type:
         logger.warning(
             f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
             f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''')

     return cls.from_dict(_A				,	**_A)



class 				A_     (    __lowercase      ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE				:      Tuple			=       "altclip"
 _SCREAMING_SNAKE_CASE				:      Optional[Any]			=       True


 def __init__(						self				,	_A=None				,	_A=None				,	_A=768				,	_A=2.6592				,	**_A)			->		Dict:

     """simple docstring"""



     _UpperCAmelCase					:   Tuple      	=					kwargs.pop('''text_config_dict'''				,	_A)
     _UpperCAmelCase					:   int      	=					kwargs.pop('''vision_config_dict'''				,	_A)

     super().__init__(**_A)

     # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
     # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
     # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
     if text_config_dict is not None:
         if text_config is None:
             _UpperCAmelCase					:   Optional[Any]      	=					{}

         # This is the complete result when using `text_config_dict`.
         _UpperCAmelCase					:   Optional[Any]      	=					AltCLIPTextConfig(**_A).to_dict()

         # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
         for key, value in _text_config_dict.items():
             if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
                 # If specified in `text_config_dict`
                 if key in text_config_dict:
                     _UpperCAmelCase					:   Union[str, Any]      	=					(
                         f'''`{key}` is found in both `text_config_dict` and `text_config` but with different values. '''
                         f'''The value `text_config_dict[\"{key}\"]` will be used instead.'''
                     )
                 # If inferred from default argument values (just to be super careful)
                 else:
                     _UpperCAmelCase					:   int      	=					(
                         f'''`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '''
                         f'''value `text_config[\"{key}\"]` will be overriden.'''
                     )
                 logger.warning(_A)

            # Update all values in `text_config` with the ones in `_text_config_dict`.
         text_config.update(_text_config_dict)

     if vision_config_dict is not None:
         if vision_config is None:
             _UpperCAmelCase					:   int      	=					{}

         # This is the complete result when using `vision_config_dict`.
         _UpperCAmelCase					:   Tuple      	=					AltCLIPVisionConfig(**_A).to_dict()
         # convert keys to string instead of integer
         if "id2label" in _vision_config_dict:
             _UpperCAmelCase					:   Optional[Any]      	=					{
                 str(_A): value for key, value in _vision_config_dict['''id2label'''].items()
             }

         # Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
         for key, value in _vision_config_dict.items():
             if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
                 # If specified in `vision_config_dict`
                 if key in vision_config_dict:
                     _UpperCAmelCase					:   str      	=					(
                         f'''`{key}` is found in both `vision_config_dict` and `vision_config` but with different '''
                         f'''values. The value `vision_config_dict[\"{key}\"]` will be used instead.'''
                     )
                 # If inferred from default argument values (just to be super careful)
                 else:
                     _UpperCAmelCase					:   List[Any]      	=					(
                         f'''`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '''
                         f'''The value `vision_config[\"{key}\"]` will be overriden.'''
                     )
                 logger.warning(_A)

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
         vision_config.update(_vision_config_dict)

     if text_config is None:
         _UpperCAmelCase					:   Any      	=					{}
         logger.info('''`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.''')

     if vision_config is None:
         _UpperCAmelCase					:   Dict      	=					{}
         logger.info('''`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.''')

     _UpperCAmelCase					:   Union[str, Any]      	=					AltCLIPTextConfig(**_A)
     _UpperCAmelCase					:   str      	=					AltCLIPVisionConfig(**_A)

     _UpperCAmelCase					:   str      	=					projection_dim
     _UpperCAmelCase					:   Any      	=					logit_scale_init_value
     _UpperCAmelCase					:   Union[str, Any]      	=					1.0


 @classmethod
 def 			snake_case__					(						cls				,	_A				,	_A				,	**_A)			->		List[str]:

     """simple docstring"""



     return cls(text_config=text_config.to_dict()				,	vision_config=vision_config.to_dict()				,	**_A)


 def 			snake_case__					(						self)			->		str:

     """simple docstring"""



     _UpperCAmelCase					:   Tuple      	=					copy.deepcopy(self.__dict__)
     _UpperCAmelCase					:   int      	=					self.text_config.to_dict()
     _UpperCAmelCase					:   Any      	=					self.vision_config.to_dict()
     _UpperCAmelCase					:   Any      	=					self.__class__.model_type
     return output












'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class  snake_case__      (					UpperCamelCase     ,				UpperCamelCase     ,				UpperCamelCase     ,				unittest.TestCase):
					a_        =					StableDiffusionInstructPixaPixPipeline
					a_        =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
					a_        =					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					a_        =					IMAGE_TO_IMAGE_IMAGE_PARAMS
					a_        =					IMAGE_TO_IMAGE_IMAGE_PARAMS




					def 		A      (  self  :		str      )    ->							Optional[Any]:
								torch.manual_seed(0      )
								UpperCAmelCase_      :       Tuple          =       UNetaDConditionModel(
								    block_out_channels=(32, 64)				, layers_per_block=2				, sample_size=32				, in_channels=8				, out_channels=4				, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				, up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				, cross_attention_dim=32				, )
								UpperCAmelCase_      :       Any          =       PNDMScheduler(skip_prk_steps=_A      )
								torch.manual_seed(0      )
								UpperCAmelCase_      :       int          =       AutoencoderKL(
								    block_out_channels=[32, 64]				, in_channels=3				, out_channels=3				, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				, up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				, latent_channels=4				, )
								torch.manual_seed(0      )
								UpperCAmelCase_      :       Tuple          =       CLIPTextConfig(
								    bos_token_id=0				, eos_token_id=2				, hidden_size=32				, intermediate_size=37				, layer_norm_eps=1e-05				, num_attention_heads=4				, num_hidden_layers=5				, pad_token_id=1				, vocab_size=10_00				, )
								UpperCAmelCase_      :       List[str]          =       CLIPTextModel(_A      )
								UpperCAmelCase_      :       List[str]          =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

								UpperCAmelCase_      :       Union[str, Any]          =       {
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components




					def 		A      (  self  :		List[str]				, _A  :		Tuple				, _A  :		str=0      )    ->							int:
								UpperCAmelCase_      :       Tuple          =       floats_tensor((1, 3, 32, 32)				, rng=random.Random(_A      )      ).to(_A      )
								UpperCAmelCase_      :       Dict          =       image.cpu().permute(0				, 2				, 3				, 1      )[0]
								UpperCAmelCase_      :       Optional[Any]          =       Image.fromarray(np.uinta(_A      )      ).convert('''RGB'''      )
								if str(_A      ).startswith('''mps'''      ):
											UpperCAmelCase_      :       str          =       torch.manual_seed(_A      )
								else:
											UpperCAmelCase_      :       Optional[Any]          =       torch.Generator(device=_A      ).manual_seed(_A      )
								UpperCAmelCase_      :       List[str]          =       {
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''image''': image,
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''image_guidance_scale''': 1,
								    '''output_type''': '''numpy''',
								}
								return inputs




					def 		A      (  self  :		Tuple      )    ->							List[Any]:
								UpperCAmelCase_      :       List[str]          =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase_      :       Any          =       self.get_dummy_components()
								UpperCAmelCase_      :       int          =       StableDiffusionInstructPixaPixPipeline(**_A      )
								UpperCAmelCase_      :       Tuple          =       sd_pipe.to(_A      )
								sd_pipe.set_progress_bar_config(disable=_A      )

								UpperCAmelCase_      :       str          =       self.get_dummy_inputs(_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       sd_pipe(**_A      ).images
								UpperCAmelCase_      :       int          =       image[0, -3:, -3:, -1]
								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase_      :       Union[str, Any]          =       np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815]      )

								assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3




					def 		A      (  self  :		int      )    ->							str:
								UpperCAmelCase_      :       List[Any]          =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase_      :       List[str]          =       self.get_dummy_components()
								UpperCAmelCase_      :       Dict          =       StableDiffusionInstructPixaPixPipeline(**_A      )
								UpperCAmelCase_      :       str          =       sd_pipe.to(_A      )
								sd_pipe.set_progress_bar_config(disable=_A      )

								UpperCAmelCase_      :       Any          =       self.get_dummy_inputs(_A      )
								UpperCAmelCase_      :       List[Any]          =       '''french fries'''
								UpperCAmelCase_      :       Optional[Any]          =       sd_pipe(**_A				, negative_prompt=_A      )
								UpperCAmelCase_      :       Tuple          =       output.images
								UpperCAmelCase_      :       Union[str, Any]          =       image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase_      :       Dict          =       np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831]      )

								assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3




					def 		A      (  self  :		Dict      )    ->							str:
								UpperCAmelCase_      :       List[str]          =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase_      :       List[Any]          =       self.get_dummy_components()
								UpperCAmelCase_      :       Any          =       StableDiffusionInstructPixaPixPipeline(**_A      )
								UpperCAmelCase_      :       int          =       sd_pipe.to(_A      )
								sd_pipe.set_progress_bar_config(disable=_A      )

								UpperCAmelCase_      :       Any          =       self.get_dummy_inputs(_A      )
								UpperCAmelCase_      :       Any          =       [inputs['''prompt''']] * 2

								UpperCAmelCase_      :       int          =       np.array(inputs['''image''']      ).astype(np.floataa      ) / 255.0
								UpperCAmelCase_      :       int          =       torch.from_numpy(_A      ).unsqueeze(0      ).to(_A      )
								UpperCAmelCase_      :       List[str]          =       image / 2 + 0.5
								UpperCAmelCase_      :       str          =       image.permute(0				, 3				, 1				, 2      )
								UpperCAmelCase_      :       Any          =       image.repeat(2				, 1				, 1				, 1      )

								UpperCAmelCase_      :       List[Any]          =       sd_pipe(**_A      ).images
								UpperCAmelCase_      :       str          =       image[-1, -3:, -3:, -1]

								assert image.shape == (2, 32, 32, 3)
								UpperCAmelCase_      :       Optional[Any]          =       np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579]      )

								assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3




					def 		A      (  self  :		List[str]      )    ->							List[Any]:
								UpperCAmelCase_      :       Union[str, Any]          =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase_      :       Tuple          =       self.get_dummy_components()
								UpperCAmelCase_      :       List[str]          =       EulerAncestralDiscreteScheduler(
								    beta_start=0.00_085				, beta_end=0.012				, beta_schedule='''scaled_linear'''      )
								UpperCAmelCase_      :       List[Any]          =       StableDiffusionInstructPixaPixPipeline(**_A      )
								UpperCAmelCase_      :       Dict          =       sd_pipe.to(_A      )
								sd_pipe.set_progress_bar_config(disable=_A      )

								UpperCAmelCase_      :       Tuple          =       self.get_dummy_inputs(_A      )
								UpperCAmelCase_      :       str          =       sd_pipe(**_A      ).images
								UpperCAmelCase_      :       Dict          =       image[0, -3:, -3:, -1]

								UpperCAmelCase_      :       Dict          =       [round(_A				, 4      ) for x in image_slice.flatten().tolist()]
								print(''','''.join([str(_A      ) for x in slice]      )      )

								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase_      :       Any          =       np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986]      )

								assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3




					def 		A      (  self  :		str      )    ->							List[str]:
								super().test_inference_batch_single_identical(expected_max_diff=3e-3      )





					def 		A      (  self  :		List[Any]      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Tuple          =       self.get_dummy_components()
								UpperCAmelCase_      :       Tuple          =       StableDiffusionInstructPixaPixPipeline(**_A      )
								UpperCAmelCase_      :       Any          =       VaeImageProcessor(do_resize=_A				, do_normalize=_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )

								UpperCAmelCase_      :       int          =       pipe(**self.get_dummy_inputs_by_type(_A				, input_image_type='''pt'''      )      )[0]

								UpperCAmelCase_      :       Union[str, Any]          =       components['''vae''']
								UpperCAmelCase_      :       Any          =       self.get_dummy_inputs_by_type(_A				, input_image_type='''pt'''      )

								for image_param in self.image_latents_params:
											if image_param in inputs.keys():
														UpperCAmelCase_      :       int          =       vae.encode(inputs[image_param]      ).latent_dist.mode()

								UpperCAmelCase_      :       List[Any]          =       pipe(**_A      )[0]

								UpperCAmelCase_      :       int          =       np.abs(out - out_latents_inputs      ).max()
								self.assertLess(_A				, 1e-4				, '''passing latents as image input generate different result from passing image'''      )






@slow
@require_torch_gpu
class  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		Any      )    ->							Optional[Any]:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




					def 		A      (  self  :		List[str]				, _A  :		Optional[Any]=0      )    ->							List[Any]:
								UpperCAmelCase_      :       Dict          =       torch.manual_seed(_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       load_image(
								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''      )
								UpperCAmelCase_      :       Optional[int]          =       {
								    '''prompt''': '''turn him into a cyborg''',
								    '''image''': image,
								    '''generator''': generator,
								    '''num_inference_steps''': 3,
								    '''guidance_scale''': 7.5,
								    '''image_guidance_scale''': 1.0,
								    '''output_type''': '''numpy''',
								}
								return inputs




					def 		A      (  self  :		Optional[int]      )    ->							Optional[Any]:
								UpperCAmelCase_      :       List[Any]          =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''				, safety_checker=_A      )
								pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )
								pipe.enable_attention_slicing()

								UpperCAmelCase_      :       List[str]          =       self.get_inputs()
								UpperCAmelCase_      :       int          =       pipe(**_A      ).images
								UpperCAmelCase_      :       Dict          =       image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 5_12, 3)
								UpperCAmelCase_      :       Any          =       np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555]      )

								assert np.abs(expected_slice - image_slice      ).max() < 1e-3




					def 		A      (  self  :		Optional[Any]      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Optional[Any]          =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''				, safety_checker=_A      )
								UpperCAmelCase_      :       Dict          =       LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
								pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )
								pipe.enable_attention_slicing()

								UpperCAmelCase_      :       List[Any]          =       self.get_inputs()
								UpperCAmelCase_      :       str          =       pipe(**_A      ).images
								UpperCAmelCase_      :       Optional[int]          =       image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 5_12, 3)
								UpperCAmelCase_      :       List[str]          =       np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301]      )

								assert np.abs(expected_slice - image_slice      ).max() < 1e-3




					def 		A      (  self  :		List[str]      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       str          =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''				, safety_checker=_A      )
								UpperCAmelCase_      :       str          =       DDIMScheduler.from_config(pipe.scheduler.config      )
								pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )
								pipe.enable_attention_slicing()

								UpperCAmelCase_      :       Any          =       self.get_inputs()
								UpperCAmelCase_      :       Any          =       pipe(**_A      ).images
								UpperCAmelCase_      :       Optional[Any]          =       image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 5_12, 3)
								UpperCAmelCase_      :       Dict          =       np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753]      )

								assert np.abs(expected_slice - image_slice      ).max() < 1e-3




					def 		A      (  self  :		Tuple      )    ->							List[Any]:
								UpperCAmelCase_      :       List[str]          =       0

								def callback_fn(_A  :		int				, _A  :		int				, _A  :		torch.FloatTensor      ) -> None:
											UpperCAmelCase_      :       Tuple          =       True
											nonlocal number_of_steps
											number_of_steps += 1
											if step == 1:
														UpperCAmelCase_      :       List[str]          =       latents.detach().cpu().numpy()
														assert latents.shape == (1, 4, 64, 64)
														UpperCAmelCase_      :       Optional[Any]          =       latents[0, -3:, -3:, -1]
														UpperCAmelCase_      :       List[Any]          =       np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983]      )

														assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2
											elif step == 2:
														UpperCAmelCase_      :       Union[str, Any]          =       latents.detach().cpu().numpy()
														assert latents.shape == (1, 4, 64, 64)
														UpperCAmelCase_      :       Dict          =       latents[0, -3:, -3:, -1]
														UpperCAmelCase_      :       Tuple          =       np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115]      )

														assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2

								UpperCAmelCase_      :       Dict          =       False

								UpperCAmelCase_      :       Dict          =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''				, safety_checker=_A				, torch_dtype=torch.floataa      )
								UpperCAmelCase_      :       Any          =       pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )
								pipe.enable_attention_slicing()

								UpperCAmelCase_      :       Dict          =       self.get_inputs()
								pipe(**_A				, callback=_A				, callback_steps=1      )
								assert callback_fn.has_been_called
								assert number_of_steps == 3




					def 		A      (  self  :		int      )    ->							int:
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								UpperCAmelCase_      :       Union[str, Any]          =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix'''				, safety_checker=_A				, torch_dtype=torch.floataa      )
								UpperCAmelCase_      :       Tuple          =       pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )
								pipe.enable_attention_slicing(1      )
								pipe.enable_sequential_cpu_offload()

								UpperCAmelCase_      :       Dict          =       self.get_inputs()
								UpperCAmelCase_      :       List[str]          =       pipe(**_A      )

								UpperCAmelCase_      :       Dict          =       torch.cuda.max_memory_allocated()
								# make sure that less than 2.2 GB is allocated
								assert mem_bytes < 2.2 * 10**9





					def 		A      (  self  :		str      )    ->							int:
								UpperCAmelCase_      :       List[Any]          =       self.get_inputs()
								# resize to resolution that is divisible by 8 but not 16 or 32
								UpperCAmelCase_      :       List[str]          =       inputs['''image'''].resize((5_04, 5_04)      )

								UpperCAmelCase_      :       int          =       '''timbrooks/instruct-pix2pix'''
								UpperCAmelCase_      :       Tuple          =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    _A				, safety_checker=_A				, )
								pipe.to(_A      )
								pipe.set_progress_bar_config(disable=_A      )
								pipe.enable_attention_slicing()

								UpperCAmelCase_      :       Optional[Any]          =       pipe(**_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       output.images[0]

								UpperCAmelCase_      :       List[str]          =       image[2_55:2_58, 3_83:3_86, -1]

								assert image.shape == (5_04, 5_04, 3)
								UpperCAmelCase_      :       List[str]          =       np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590]      )

								assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5e-3


